• DocumentCode
    468982
  • Title

    The fault diagnosis system with self-repair function for screw oil pump based on wavelet neural network

  • Author

    Tian, Jing-wen ; Gao, Mei-juan ; Zhou, Hao ; Li, Kai

  • Author_Institution
    Beijing Union Univ., Beijing
  • Volume
    2
  • fYear
    2007
  • fDate
    2-4 Nov. 2007
  • Firstpage
    699
  • Lastpage
    704
  • Abstract
    Considering the issues that the relationship between the fault of screw oil pump existent and fault information is a complicated and nonlinear system, and the wavelet neural network has the advantages of both wavelet analysis and neural network, a fault diagnosis system with self-repair function for screw oil pump based on wavelet neural network is presented in this paper. Moreover, we adopt a method of reduce the number of the wavelet basic function by analysis the sparse property of sample data, and the learning algorithm based on the gradient descent was used to train network. With the ability of strong self-learning and function approach and fast convergence rate of wavelet neural network, the diagnosis system can truly diagnose the fault of screw oil pump by learning the fault information. The real diagnosis results show that this system is feasible and effective.
  • Keywords
    fault diagnosis; gradient methods; learning (artificial intelligence); neural nets; nonlinear systems; petroleum industry; pumps; self-adjusting systems; wavelet transforms; complicated system; fault diagnosis system; gradient descent; learning algorithm; network training; nonlinear system; screw oil pump; self-learning approach; self-repair function; wavelet basic function; wavelet neural network; Artificial neural networks; Chemical analysis; Fasteners; Fault diagnosis; Information analysis; Neural networks; Pattern analysis; Petroleum; Production; Wavelet analysis; Fault diagnosis; screw oil pump; self-repair function; wavelet neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-1065-1
  • Electronic_ISBN
    978-1-4244-1066-8
  • Type

    conf

  • DOI
    10.1109/ICWAPR.2007.4420759
  • Filename
    4420759